Abstract This paper presents a model of contrastive explanation using structural casual models. The topic causal in artificial intelligence has gathered interest recent years as researchers and practitioners aim to increase trust understanding intelligent decision-making. While different sub-fields have looked into this problem with sub-field-specific view, there are few models that capture mor...

This paper deals with nonparametric density estimation on $\mathbb{R}^2$ from i.i.d. observations. It is assumed that after unknown rotation of the coordinate system coordinates observations are independent random variables whose densities belong to a Hölder class parameters. The minimax and adaptive theories for this structural statistical model developed.

MOTIVATION The study of carbohydrate sugar chains, or glycans, has been one of slow progress mainly due to the difficulty in establishing standard methods for analyzing their structures and biosynthesis. Glycans are generally tree structures that are more complex than linear DNA or protein sequences, and evidence shows that patterns in glycans may be present that spread across siblings and into...

Structural equation model (SEM) trees are data-driven tools for finding variables that predict group differences in SEM parameters. build upon the decision tree paradigm by growing structures divide a data set recursively into homogeneous subsets. In past research, have been estimated predominantly with R package semtree . The original algorithm selects split among covariates calculating likeli...